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Received — 8 April 2026 ⏭

Search, Do not Guess: Teaching Small Language Models to Be Effective Search Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yizhou Liu · Qi Sun · Yulin Chen · Siyue Zhang · Chen Zhao
7 April 2026 at 12:00
arXiv:2604.04651v1 Announce Type: new Abstract: Agents equipped with search tools have emerged as effective solutions for knowledge-intensive tasks. While Large Language Models (LLMs) exhibit strong reasoning capabilities, their high computational cost limits practical deployment for search agents. Consequently, recent work has focused on distilling agentic behaviors from LLMs into Small Language Models (SLMs). Through comprehensive evaluation on complex multi-hop reasoning tasks, we find that despite possessing less parametric knowledge, SLMs invoke search tools less frequently and are more prone to hallucinations. To address this issue, we propose \policy, a lightweight fine-tuning approach that explicitly trains SLMs to reliably retrieve and generate answers grounded in retrieved evidence. Compared to agent distillation from LLMs, our approach improves performance by 17.3 scores on Bamboogle and 15.3 scores on HotpotQA, achieving LLM-level results across benchmarks. Our further analysis reveals that adaptive search strategies in SLMs often degrade performance, highlighting the necessity of consistent search behavior for reliable reasoning.

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoyu Dong · Pengkun Zhang · Yan Gao · Xuanyu Dong · Yilin Cheng · Mingzhe Lu · Zikun Zhu · Adina Yakefu · Shuxin Zheng
7 April 2026 at 12:00
arXiv:2512.13168v4 Announce Type: replace Abstract: We introduce FinWorkBench (a.k.a. Finch), a benchmark for evaluating agents on real-world, enterprise-grade finance and accounting workflows that interleave data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is built from authentic enterprise workspaces from Enron (15,000 files and 500,000 emails) and other financial institutions spanning 2000 to 2025, preserving the in-the-wild messiness of multimodal artifacts such as tables and charts across diverse domains including budgeting, trading, and asset management. We propose a workflow construction process that combines LLM-assisted mining of workflows from authentic enterprise environments with expert annotation. Specifically, we use LLM-assisted, expert-verified derivation of workflows from real-world email threads and spreadsheet version histories, followed by meticulous workflow annotation requiring more than 700 hours of expert effort. This process yields 172 composite workflows with 384 tasks, involving 1,710 spreadsheets with 27 million cells, along with PDFs and other artifacts, capturing the intrinsically messy, long-horizon, knowledge-intensive, and collaborative nature of enterprise work. We conduct both human and automated evaluations of frontier AI systems, including GPT 5.1, Claude Sonnet/Opus 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max. GPT 5.1 Pro spends an average of 16.8 minutes per workflow yet passes only 38.4% of workflows. Comprehensive case studies further highlight the challenges that real-world enterprise workflows pose for AI agents.

VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sibo Wang · Xiangkui Cao · Jie Zhang · Zheng Yuan · Shiguang Shan · Xilin Chen · Wen Gao
7 April 2026 at 12:00
arXiv:2406.14194v3 Announce Type: replace-cross Abstract: The emergence of Large Vision-Language Models (LVLMs) marks significant strides towards achieving general artificial intelligence. However, these advancements are accompanied by concerns about biased outputs, a challenge that has yet to be thoroughly explored. Existing benchmarks are not sufficiently comprehensive in evaluating biases due to their limited data scale, single questioning format and narrow sources of bias. To address this problem, we introduce VLBiasBench, a comprehensive benchmark designed to evaluate biases in LVLMs. VLBiasBench, features a dataset that covers nine distinct categories of social biases, including age, disability status, gender, nationality, physical appearance, race, religion, profession, social economic status, as well as two intersectional bias categories: race x gender and race x social economic status. To build a large-scale dataset, we use Stable Diffusion XL model to generate 46,848 high-quality images, which are combined with various questions to creat 128,342 samples. These questions are divided into open-ended and close-ended types, ensuring thorough consideration of bias sources and a comprehensive evaluation of LVLM biases from multiple perspectives. We conduct extensive evaluations on 15 open-source models as well as two advanced closed-source models, yielding new insights into the biases present in these models. Our benchmark is available at https://github.com/Xiangkui-Cao/VLBiasBench.
Received — 2 April 2026 ⏭

A monocyte-centered framework for predicting immunochemotherapy efficacy in lung squamous cell carcinoma patients

✇Omics In Lung
By: Jiangnan Zhao · Zijin Wang · Manyu Xiao · Yeyuan Zhang · Boxin Liu · Silin Chen · Yiping Tian · Dongqing Lv · Pingli Wang · Hai Song · Yuefeng Wu · Jian Liu · Kai Wang
30 March 2026 at 18:00

EMBO Mol Med. 2026 Mar 30. doi: 10.1038/s44321-026-00410-y. Online ahead of print.

ABSTRACT

Lung cancer is the leading cause of cancer-related mortality worldwide, with lung squamous cell carcinoma (LUSC) comprising 20-30% of cases. Immunochemotherapy (IC) is the standard first-line treatment for advanced LUSC, yet reliable predictors of therapeutic response remain unavailable. Using single-cell multi-omics profiling of paired pre- and post-treatment tumor and blood samples, we observed that patients responding to IC exhibited significantly higher baseline levels of peripheral blood monocytes, tumor-infiltrating classical monocytes, and APOBEC3A+ monocytes across both compartments compared with non-responders. These associations were independently validated in additional cohorts using routine complete blood count testing and multiplex immunofluorescence analysis of native tumor tissues. Our findings reveal monocyte-related parameters as clinically accessible indicators that link systemic immunity with the tumor microenvironment and hold promise for predicting IC responsiveness in patients with LUSC.

PMID:41912871 | DOI:10.1038/s44321-026-00410-y

A monocyte-centered framework for predicting immunochemotherapy efficacy in lung squamous cell carcinoma patients

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Jiangnan Zhao · Zijin Wang · Manyu Xiao · Yeyuan Zhang · Boxin Liu · Silin Chen · Yiping Tian · Dongqing Lv · Pingli Wang · Hai Song · Yuefeng Wu · Jian Liu · Kai Wang
30 March 2026 at 18:00

EMBO Mol Med. 2026 Mar 30. doi: 10.1038/s44321-026-00410-y. Online ahead of print.

ABSTRACT

Lung cancer is the leading cause of cancer-related mortality worldwide, with lung squamous cell carcinoma (LUSC) comprising 20-30% of cases. Immunochemotherapy (IC) is the standard first-line treatment for advanced LUSC, yet reliable predictors of therapeutic response remain unavailable. Using single-cell multi-omics profiling of paired pre- and post-treatment tumor and blood samples, we observed that patients responding to IC exhibited significantly higher baseline levels of peripheral blood monocytes, tumor-infiltrating classical monocytes, and APOBEC3A+ monocytes across both compartments compared with non-responders. These associations were independently validated in additional cohorts using routine complete blood count testing and multiplex immunofluorescence analysis of native tumor tissues. Our findings reveal monocyte-related parameters as clinically accessible indicators that link systemic immunity with the tumor microenvironment and hold promise for predicting IC responsiveness in patients with LUSC.

PMID:41912871 | DOI:10.1038/s44321-026-00410-y

Received — 26 March 2026 ⏭

Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenxuan Huang · Yu Zeng · Qiuchen Wang · Zhen Fang · Shaosheng Cao · Zheng Chu · Qingyu Yin · Shuang Chen · Zhenfei Yin · Lin Chen · Zehui Chen · Xu Tang · Yao Hu · Shaohui Lin · Philip Torr · Feng Zhao · Wanli Ouyang
25 March 2026 at 12:00
arXiv:2601.22060v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by ``reasoning-then-tool-call'' for visual and textual search engines to obtain substantial gains on tasks requiring extensive factual information. However, these approaches typically define multimodal search in a naive setting, assuming that a single full-level or entity-level image query and few text query suffices to retrieve the key evidence needed to answer the question, which is unrealistic in real-world scenarios with substantial visual noise. Moreover, they are often limited in the reasoning depth and search breadth, making it difficult to solve complex questions that require aggregating evidence from diverse visual and textual sources. Building on this, we propose Vision-DeepResearch, which proposes one new multimodal deep-research paradigm, i.e., performs multi-turn, multi-entity and multi-scale visual and textual search to robustly hit real-world search engines under heavy noise. Our Vision-DeepResearch supports dozens of reasoning steps and hundreds of engine interactions, while internalizing deep-research capabilities into the MLLM via cold-start supervision and RL training, resulting in a strong end-to-end multimodal deep-research MLLM. It substantially outperforming existing multimodal deep-research MLLMs, and workflows built on strong closed-source foundation model such as GPT-5, Gemini-2.5-pro and Claude-4-Sonnet. The code will be released in https://github.com/Osilly/Vision-DeepResearch.

From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenhao Wu · Zhentao Tang · Yafu Li · Shixiong Kai · Mingxuan Yuan · Chunlin Chen · Zhi Wang
25 March 2026 at 12:00
arXiv:2603.03292v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields. While Retrieval-Augmented Generation (RAG) mitigates these issues, existing methods rely on noisy token-level signals and lack the multi-round refinement required for complex reasoning. In the paper, we propose MA-RAG (Multi-Round Agentic RAG), a framework that facilitates test-time scaling for complex medical reasoning by iteratively evolving both external evidence and internal reasoning history within an agentic refinement loop. At each round, the agent transforms semantic conflict among candidate responses into actionable queries to retrieve external evidence, while optimizing history reasoning traces to mitigate long-context degradation. MA-RAG extends the self-consistency principle by leveraging the lack of consistency as a proactive signal for multi-round agentic reasoning and retrieval, and mirrors a boosting mechanism that iteratively minimizes the residual error toward a stable, high-fidelity medical consensus. Extensive evaluations across 7 medical Q&A benchmarks show that MA-RAG consistently surpasses competitive inference-time scaling and RAG baselines, delivering substantial +6.8 points on average accuracy over the backbone model. Our code is available at https://github.com/NJU-RL/MA-RAG.

Research on the compatibility mechanism of the Tingli Dazao Xiefei Decoction by multi-organ metabolomics strategy

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Rui-Feng Wang · Pei-Pei Yuan · Long Chen · Zhen-Kai Zhang · Bing-Xian Zhang · Lin Chen · Shu-Miao Yuan · Yan-Fang Zhao · Ning Zhou · Xiao-Ke Zheng · Wei-Sheng Feng
23 March 2026 at 18:00

J Ethnopharmacol. 2026 Mar 21:121548. doi: 10.1016/j.jep.2026.121548. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: The Tingli Dazao Xiefei Decoction (TD) is a traditional phlegm-eliminating prescription composed of Descurainia sophia (L.) Webb. ex Prantl (TLZ) and Ziziphus jujuba Mill. (DZ), which can relieve lung, heart and kidney injury in asthma. TLZ acts as the monarch drug in the TD. Based on the research mode of "material basis of traditional Chinese medicinal properties can be divided and combined", we have confirmed that the flavonoid glycosides components /the oligosaccharide components/the fatty oil component (FG/Oli/FO) are effective components of TLZ. However, the compatibility mechanism of the TD, and the contribution of the effective components of TLZ to the efficacy were still unclear.

AIM OF THE STUDY: To clarify the compatibility mechanism of TD, and the contribution of the effective components of TLZ to the efficacy from a comprehensive perspective of lung, heart, and kidney.

METHODS: First, we chose the asthma model corresponding to the efficacy of TD in purging the lungs and relieving asthma, and the rats were divided into the normal (NC) group, model (M) group, dexamethasone (DEX) group, and treatment groups of TD/TLZ/DZ/FO+DZ/Oli+DZ/FG+DZ. Second, metabolomics and network pharmacology were applied to elucidate the comprehensive protective effect of TD/FG+DZ/Oli+DZ/FO+DZ. Third, the multi-omics results were validated using Western blotting, RT-qPCR, flow cytometry, and immunofluorescence.

RESULTS: FO+DZ/Oli+DZ/FG+DZ had different degrees of protective effects against lung/heart/kidney injury in asthma. In metabolomics research, the principal component analysis (PCA) and cluster analysis results showed that the TLZ group was closer to TD group than DZ group, the FO+DZ and Oli+DZ group clustered with TD/NC groups in the lung and kidney, and the FO+DZ and FG+DZ group clustered with TD/NC groups in the heart. Pathway enrichment analysis suggested that the comprehensive protective effect of TLZ and its effective components combined with DZ on lung/heart/kidney may be achieved by regulating the arginine and proline metabolism, alanine, aspartate and glutamate metabolism, and unsaturated fatty acid biosynthesis. Multi-organ metabolomics and network pharmacology revealed consistent biological functions in KEGG pathways. Validation experiment showed that TLZ and its effective components combined with DZ could reverse the abnormal expression of proteins and RNA related to inflammation, airway remodeling, excitotoxicity, and energy-supply, apoptosis at different levels. Furthermore, FO+DZ may reduce asthma damage by inhibiting the FABP4/PPAR-γ/NF-κB signaling pathway.

CONCLUSION: TLZ played the key role in TD, and FO had the best therapeutic effect on each organ; the efficacy of Oli was mainly reflected in reducing lung and kidney damage, and FG was mainly involved in enhancing energy metabolism in the heart. These findings proved that traditional Chinese medicine could exert comprehensive efficacy in a 'multi-components trigger multi-channel' way.

PMID:41871629 | DOI:10.1016/j.jep.2026.121548

Fluid-Derived Organoids from Pleural Effusion and Ascites: Emerging Models for Drug Resistance and Personalized Oncology

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yongyang Chen · Xiaoqing Xu · Jialin Chen · Miao Yin · Jinhui Chen · Zhanghua Qi · Ming Shi · Wenmei Su
23 March 2026 at 18:00

J Cancer. 2026 Mar 4;17(3):614-625. doi: 10.7150/jca.127511. eCollection 2026.

ABSTRACT

Malignant pleural effusion (MPE) and malignant ascites (MA) are common complications in advanced-stage cancers, often signifying disease progression and resistance to treatment. Compared to tissue biopsies or surgical specimens, materials derived from effusions offer advantages such as minimal invasiveness, ease of accessibility, and the feasibility of repeated collection during therapeutic interventions. Organoids generated from tumor cells in effusions, termed fluid-derived organoids (FDOs), have demonstrated the ability to maintain genetic heterogeneity and accurately replicate patient-specific tumor phenotypes. These characteristics position FDOs as promising models for investigating drug resistance mechanisms and informing personalized oncology strategies. In the context of lung cancer, organoids derived from pleural effusions have been employed to study acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors and immunotherapy. Similarly, in ovarian and gastrointestinal cancers, organoids derived from ascites have proven to be valuable platforms for examining chemotherapy resistance and conducting drug sensitivity testing. FDOs have shown significant potential for translational applications by effectively correlating ex vivo drug responses with clinical outcomes, thus facilitating real-time monitoring of resistance evolution. However, several challenges remain, such as achieving culture standardization, maintaining the integrity of tumor microenvironment components, and integrating with multi-omics approaches. This review provides a comprehensive overview of recent advancements in the use of pleural effusion- and ascites-derived organoids for drug resistance research, underscores their applications in personalized oncology, and explores future research directions.

PMID:41869438 | PMC:PMC13003542 | DOI:10.7150/jca.127511

Research on the compatibility mechanism of the Tingli Dazao Xiefei Decoction by multi-organ metabolomics strategy

✇Omics In Lung
By: Rui-Feng Wang · Pei-Pei Yuan · Long Chen · Zhen-Kai Zhang · Bing-Xian Zhang · Lin Chen · Shu-Miao Yuan · Yan-Fang Zhao · Ning Zhou · Xiao-Ke Zheng · Wei-Sheng Feng
23 March 2026 at 18:00

J Ethnopharmacol. 2026 Mar 21:121548. doi: 10.1016/j.jep.2026.121548. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: The Tingli Dazao Xiefei Decoction (TD) is a traditional phlegm-eliminating prescription composed of Descurainia sophia (L.) Webb. ex Prantl (TLZ) and Ziziphus jujuba Mill. (DZ), which can relieve lung, heart and kidney injury in asthma. TLZ acts as the monarch drug in the TD. Based on the research mode of "material basis of traditional Chinese medicinal properties can be divided and combined", we have confirmed that the flavonoid glycosides components /the oligosaccharide components/the fatty oil component (FG/Oli/FO) are effective components of TLZ. However, the compatibility mechanism of the TD, and the contribution of the effective components of TLZ to the efficacy were still unclear.

AIM OF THE STUDY: To clarify the compatibility mechanism of TD, and the contribution of the effective components of TLZ to the efficacy from a comprehensive perspective of lung, heart, and kidney.

METHODS: First, we chose the asthma model corresponding to the efficacy of TD in purging the lungs and relieving asthma, and the rats were divided into the normal (NC) group, model (M) group, dexamethasone (DEX) group, and treatment groups of TD/TLZ/DZ/FO+DZ/Oli+DZ/FG+DZ. Second, metabolomics and network pharmacology were applied to elucidate the comprehensive protective effect of TD/FG+DZ/Oli+DZ/FO+DZ. Third, the multi-omics results were validated using Western blotting, RT-qPCR, flow cytometry, and immunofluorescence.

RESULTS: FO+DZ/Oli+DZ/FG+DZ had different degrees of protective effects against lung/heart/kidney injury in asthma. In metabolomics research, the principal component analysis (PCA) and cluster analysis results showed that the TLZ group was closer to TD group than DZ group, the FO+DZ and Oli+DZ group clustered with TD/NC groups in the lung and kidney, and the FO+DZ and FG+DZ group clustered with TD/NC groups in the heart. Pathway enrichment analysis suggested that the comprehensive protective effect of TLZ and its effective components combined with DZ on lung/heart/kidney may be achieved by regulating the arginine and proline metabolism, alanine, aspartate and glutamate metabolism, and unsaturated fatty acid biosynthesis. Multi-organ metabolomics and network pharmacology revealed consistent biological functions in KEGG pathways. Validation experiment showed that TLZ and its effective components combined with DZ could reverse the abnormal expression of proteins and RNA related to inflammation, airway remodeling, excitotoxicity, and energy-supply, apoptosis at different levels. Furthermore, FO+DZ may reduce asthma damage by inhibiting the FABP4/PPAR-γ/NF-κB signaling pathway.

CONCLUSION: TLZ played the key role in TD, and FO had the best therapeutic effect on each organ; the efficacy of Oli was mainly reflected in reducing lung and kidney damage, and FG was mainly involved in enhancing energy metabolism in the heart. These findings proved that traditional Chinese medicine could exert comprehensive efficacy in a 'multi-components trigger multi-channel' way.

PMID:41871629 | DOI:10.1016/j.jep.2026.121548

Fluid-Derived Organoids from Pleural Effusion and Ascites: Emerging Models for Drug Resistance and Personalized Oncology

✇Omics In Lung
By: Yongyang Chen · Xiaoqing Xu · Jialin Chen · Miao Yin · Jinhui Chen · Zhanghua Qi · Ming Shi · Wenmei Su
23 March 2026 at 18:00

J Cancer. 2026 Mar 4;17(3):614-625. doi: 10.7150/jca.127511. eCollection 2026.

ABSTRACT

Malignant pleural effusion (MPE) and malignant ascites (MA) are common complications in advanced-stage cancers, often signifying disease progression and resistance to treatment. Compared to tissue biopsies or surgical specimens, materials derived from effusions offer advantages such as minimal invasiveness, ease of accessibility, and the feasibility of repeated collection during therapeutic interventions. Organoids generated from tumor cells in effusions, termed fluid-derived organoids (FDOs), have demonstrated the ability to maintain genetic heterogeneity and accurately replicate patient-specific tumor phenotypes. These characteristics position FDOs as promising models for investigating drug resistance mechanisms and informing personalized oncology strategies. In the context of lung cancer, organoids derived from pleural effusions have been employed to study acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors and immunotherapy. Similarly, in ovarian and gastrointestinal cancers, organoids derived from ascites have proven to be valuable platforms for examining chemotherapy resistance and conducting drug sensitivity testing. FDOs have shown significant potential for translational applications by effectively correlating ex vivo drug responses with clinical outcomes, thus facilitating real-time monitoring of resistance evolution. However, several challenges remain, such as achieving culture standardization, maintaining the integrity of tumor microenvironment components, and integrating with multi-omics approaches. This review provides a comprehensive overview of recent advancements in the use of pleural effusion- and ascites-derived organoids for drug resistance research, underscores their applications in personalized oncology, and explores future research directions.

PMID:41869438 | PMC:PMC13003542 | DOI:10.7150/jca.127511

Received — 11 March 2026 ⏭

Lysophosphatidylcholine acyltransferase 1 promotes head and neck squamous cell carcinoma progression by enhancing COX17-dependent oxidative phosphorylation

✇Cell Death Discovery nature.com science feeds
By: Yuanyang Zhao · Yun Li · Yanshi Li · Zhihai Wang · Chuan Liu · Lin Chen · Min Wang · Mengna Wang · Zhaobo Cheng · Guohua Hu · Min Pan
6 March 2026 at 08:00

Cell Death Discovery, Published online: 06 March 2026; doi:10.1038/s41420-026-02994-3

Lysophosphatidylcholine acyltransferase 1 promotes head and neck squamous cell carcinoma progression by enhancing COX17-dependent oxidative phosphorylation
Received — 5 March 2026 ⏭

From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenhao Wu · Zhentao Tang · Yafu Li · Shixiong Kai · Mingxuan Yuan · Zhenhong Sun · Chunlin Chen · Zhi Wang
5 March 2026 at 13:00
arXiv:2603.03292v1 Announce Type: cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields. While Retrieval-Augmented Generation (RAG) mitigates these issues, existing methods rely on noisy token-level signals and lack the multi-round refinement required for complex reasoning. In the paper, we propose **MA-RAG** (**M**ulti-Round **A**gentic RAG), a framework that facilitates test-time scaling for complex medical reasoning by iteratively evolving both external evidence and internal reasoning history within an agentic refinement loop. At each round, the agent transforms semantic **conflict** among candidate responses into actionable queries to retrieve external evidence, while optimizing history reasoning traces to mitigate long-context degradation. MA-RAG extends the *self-consistency* principle by leveraging the lack of consistency as a proactive signal for multi-round agentic reasoning and retrieval, and mirrors a *boosting* mechanism that iteratively minimizes the residual error toward a stable, high-fidelity medical **consensus**. Extensive evaluations across 7 medical Q&A benchmarks show that MA-RAG consistently surpasses competitive inference-time scaling and RAG baselines, delivering **substantial +6.8 points** on average accuracy over the backbone model. Our code is available at [this url](https://github.com/NJU-RL/MA-RAG).

Understanding Parents' Desires in Moderating Children's Interactions with GenAI Chatbots through LLM-Generated Probes

✇cs.AI, q-bio.NC updates on arXiv.org
By: John Driscoll · Yulin Chen · Viki Shi · Izak Vucharatavintara · Yaxing Yao · Haojian Jin
5 March 2026 at 13:00
arXiv:2603.03727v1 Announce Type: cross Abstract: This paper studies how parents want to moderate children's interactions with Generative AI chatbots, with the goal of informing the design of future GenAI parental control tools. We first used an LLM to generate synthetic child-GenAI chatbot interaction scenarios and worked with four parents to validate their realism. From this dataset, we carefully selected 12 diverse examples that evoked varying levels of concern and were rated the most realistic. Each example included a prompt and a GenAI chatbot response. We presented these to parents (N=24) and asked whether they found them concerning, why, and how they would prefer the responses to be modified and communicated. Our findings reveal three key insights: (1) parents express concern about interactions that current GenAI chatbot parental controls neglect; (2) parents want fine-grained transparency and moderation at the conversation level; and (3) parents need personalized controls that adapt to their desired strategies and children's ages.
Received — 25 February 2026 ⏭

Prompt Optimization Via Diffusion Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shiyu Wang · Haolin Chen · Liangwei Yang · Jielin Qiu · Rithesh Murthy · Ming Zhu · Zixiang Chen · Silvio Savarese · Caiming Xiong · Shelby Heinecke · Huan Wang
24 February 2026 at 13:00
arXiv:2602.18449v1 Announce Type: cross Abstract: We propose a diffusion-based framework for prompt optimization that leverages Diffusion Language Models (DLMs) to iteratively refine system prompts through masked denoising. By conditioning on interaction traces, including user queries, model responses, and optional feedback, our method enables flexible, span-level prompt updates without requiring gradient access or modifying the downstream language model. Across diverse benchmarks (e.g., $\tau$-bench, SST-2, SST-5), DLM-optimized prompts consistently improve the performance of a frozen target LLM (e.g., GPT-4o-mini). We further show that moderate diffusion step counts provide the best balance between refinement quality and stability. These results highlight diffusion-based prompt optimization as a general, model-agnostic, and scalable approach for enhancing LLM performance through iterative prompt refinement.

Diversity-Incentivized Exploration for Versatile Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zican Hu · Shilin Zhang · Yafu Li · Jianhao Yan · Xuyang Hu · Leyang Cui · Xiaoye Qu · Chunlin Chen · Yu Cheng · Zhi Wang
24 February 2026 at 13:00
arXiv:2509.26209v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a crucial paradigm for incentivizing reasoning capabilities in Large Language Models (LLMs). Due to vast state-action spaces and reward sparsity in reasoning tasks, existing methods often struggle with deficient exploration and poor sample efficiency. In the paper, we propose \textbf{DIVER} (\textbf{D}iversity-\textbf{I}ncentivized Exploration for \textbf{V}ersatil\textbf{E} \textbf{R}easoning), an innovative framework that highlights the pivotal role of global sequence-level diversity to incentivize deep exploration for versatile reasoning. We first conduct a primary empirical study to reveal a strong positive correlation between global diversity and reasoning capacity. Building on this insight, we introduce global diversity incentives as an intrinsic reward to promote deep exploration in a semantically structured space. Incorporating the intrinsic reward, we develop a potential-based reward shaping mechanism to preserve optimal policy invariance and design simple heuristics to mitigate possible reward hacking. Experimental results show that DIVER outperforms competitive RLVR baselines with various exploration strategies on both in-domain and out-of-domain tasks, excelling in both Pass@1 and Pass@k evaluations. Our code is available at https://github.com/NJU-RL/DIVER.

ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haibo Tong · Feifei Zhao · Linghao Feng · Ruoyu Wu · Ruolin Chen · Lu Jia · Zhou Zhao · Jindong Li · Tenglong Li · Erliang Lin · Shuai Yang · Enmeng Lu · Yinqian Sun · Qian Zhang · Zizhe Ruan · Jinyu Fan · Zeyang Yue · Ping Wu · Huangrui Li · Chengyi Sun · Yi Zeng
24 February 2026 at 13:00
arXiv:2602.14135v3 Announce Type: replace Abstract: Rapidly evolving AI exhibits increasingly strong autonomy and goal-directed capabilities, accompanied by derivative systemic risks that are more unpredictable, difficult to control, and potentially irreversible. However, current AI safety evaluation systems suffer from critical limitations such as restricted risk dimensions and failed frontier risk detection. The lagging safety benchmarks and alignment technologies can hardly address the complex challenges posed by cutting-edge AI models. To bridge this gap, we propose the "ForesightSafety Bench" AI Safety Evaluation Framework, beginning with 7 major Fundamental Safety pillars and progressively extends to advanced Embodied AI Safety, AI4Science Safety, Social and Environmental AI risks, Catastrophic and Existential Risks, as well as 8 critical industrial safety domains, forming a total of 94 refined risk dimensions. To date, the benchmark has accumulated tens of thousands of structured risk data points and assessment results, establishing a widely encompassing, hierarchically clear, and dynamically evolving AI safety evaluation framework. Based on this benchmark, we conduct systematic evaluation and in-depth analysis of over twenty mainstream advanced large models, identifying key risk patterns and their capability boundaries. The safety capability evaluation results reveals the widespread safety vulnerabilities of frontier AI across multiple pillars, particularly focusing on Risky Agentic Autonomy, AI4Science Safety, Embodied AI Safety, Social AI Safety and Catastrophic and Existential Risks. Our benchmark is released at https://github.com/Beijing-AISI/ForesightSafety-Bench. The project website is available at https://foresightsafety-bench.beijing-aisi.ac.cn/.

Towards A Universal Graph Structural Encoder

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jialin Chen · Haolan Zuo · Haoyu Peter Wang · Siqi Miao · Pan Li · Rex Ying
24 February 2026 at 13:00
arXiv:2504.10917v2 Announce Type: replace-cross Abstract: Recent advancements in large-scale pre-training have shown the potential to learn generalizable representations for downstream tasks. In the graph domain, however, capturing and transferring structural information across different graph domains remains challenging, primarily due to the inherent differences in graph topological patterns across various contexts. For example, a social network's structure is fundamentally different from that of a product co-purchase graph. Additionally, most existing models struggle to capture the rich topological complexity of graph structures, leading to inadequate exploration of the graph embedding space. To address these challenges, we propose GFSE, a universal pre-trained graph encoder designed to capture transferable structural patterns across diverse domains such as the web graph, social networks, and citation networks. GFSE is the first cross-domain graph structural encoder pre-trained with multiple self-supervised learning objectives. Built on a Graph Transformer, GFSE incorporates attention mechanisms informed by graph structural information, enabling it to encode intricate multi-level and fine-grained topological features within complex graph structures. The pre-trained GFSE produces generic and theoretically expressive positional and structural encoding for graphs, which can be seamlessly integrated with various downstream graph feature encoders, including graph neural networks for vectorized features and Large Language Models (LLMs) for text-attributed graphs. Comprehensive experiments on synthetic and real-world datasets demonstrate GFSE's capability to significantly enhance the model's performance while requiring substantially less task-specific fine-tuning.

Generative Reasoning Re-ranker

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mingfu Liang · Yufei Li · Jay Xu · Kavosh Asadi · Xi Liu · Shuo Gu · Kaushik Rangadurai · Frank Shyu · Shuaiwen Wang · Song Yang · Zhijing Li · Jiang Liu · Mengying Sun · Fei Tian · Xiaohan Wei · Chonglin Sun · Jacob Tao · Shike Mei · Wenlin Chen · Santanu Kolay · Sandeep Pandey · Hamed Firooz · Luke Simon
24 February 2026 at 13:00
arXiv:2602.07774v4 Announce Type: replace-cross Abstract: Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge. However, existing work has three key limitations: (1) most efforts focus on retrieval and ranking, while the reranking phase, critical for refining final recommendations, is largely overlooked; (2) LLMs are typically used in zero-shot or supervised fine-tuning settings, leaving their reasoning abilities, especially those enhanced through reinforcement learning (RL) and high-quality reasoning data, underexploited; (3) items are commonly represented by non-semantic IDs, creating major scalability challenges in industrial systems with billions of identifiers. To address these gaps, we propose the Generative Reasoning Reranker (GR2), an end-to-end framework with a three-stage training pipeline tailored for reranking. First, a pretrained LLM is mid-trained on semantic IDs encoded from non-semantic IDs via a tokenizer achieving $\ge$99% uniqueness. Next, a stronger larger-scale LLM generates high-quality reasoning traces through carefully designed prompting and rejection sampling, which are used for supervised fine-tuning to impart foundational reasoning skills. Finally, we apply Decoupled Clip and Dynamic sAmpling Policy Optimization (DAPO), enabling scalable RL supervision with verifiable rewards designed specifically for reranking. Experiments on two real-world datasets demonstrate GR2's effectiveness: it surpasses the state-of-the-art OneRec-Think by 2.4% in Recall@5 and 1.3% in NDCG@5. Ablations confirm that advanced reasoning traces yield substantial gains across metrics. We further find that RL reward design is crucial in reranking: LLMs tend to exploit reward hacking by preserving item order, motivating conditional verifiable rewards to mitigate this behavior and optimize reranking performance.
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